Long noncoding RNA GATA2-AS1 augments endothelial hypoxia inducible factor 1-α induction and regulates hypoxic signaling

Vascular endothelial cells form the inner cellular lining of blood vessels and have myriad physiologic functions including angiogenesis and response to hypoxia. We recently identified a set of endothelial cell (EC)-enriched long noncoding RNAs (lncRNAs) in differentiated human primary cell types and described the role of the STEEL lncRNA in angiogenic patterning. We sought to further understand the role of EC-enriched lncRNAs in physiologic adaptation of the vascular endothelium. In this work, we describe an abundant, cytoplasmic, and EC-enriched lncRNA, GATA2-AS1, that is divergently transcribed from the EC-enriched developmental regulator, GATA2. While GATA2-AS1 is largely coexpressed with GATA2 in ECs, GATA2-AS1 and GATA2 appear to be complementary rather than synergistic as they have mostly distinct target genes. Common single nucleotide variants in GATA2-AS1 exons are associated with early-onset coronary artery disease and decreased expression of GATA2-AS1 in endothelial cell lines. In most cells, HIF1-α is central to the transcriptional response to hypoxia, while in ECs, both HIF1-α and HIF2-α are required to coordinate an acute and chronic response, respectively. In this setting, GATA2-AS1 contributes to the “HIF switch” and augments HIF1-α induction in acute hypoxia to regulate HIF1-α/HIF2-α balance. In hypoxia, GATA2-AS1 orchestrates HIF1-α-dependent induction of the glycolytic pathway and HIF1-α-independent maintenance of mitochondrial biogenesis. Similarly, GATA2-AS1 coordinates both metabolism and “tip/stalk” cell signaling to regulate angiogenesis in hypoxic ECs. Furthermore, we find that GATA2-AS1 expression patterns are perturbed in atherosclerotic disease. Together, these results define a role for GATA2-AS1 in the EC-specific response to hypoxia.

Vascular endothelial cells form the inner cellular lining of blood vessels and have myriad physiologic functions including angiogenesis and response to hypoxia. We recently identified a set of endothelial cell (EC)-enriched long noncoding RNAs (lncRNAs) in differentiated human primary cell types and described the role of the STEEL lncRNA in angiogenic patterning. We sought to further understand the role of EC-enriched lncRNAs in physiologic adaptation of the vascular endothelium. In this work, we describe an abundant, cytoplasmic, and EC-enriched lncRNA, GATA2-AS1, that is divergently transcribed from the EC-enriched developmental regulator, GATA2. While GATA2-AS1 is largely coexpressed with GATA2 in ECs, GATA2-AS1 and GATA2 appear to be complementary rather than synergistic as they have mostly distinct target genes. Common single nucleotide variants in GATA2-AS1 exons are associated with early-onset coronary artery disease and decreased expression of GATA2-AS1 in endothelial cell lines. In most cells, HIF1-α is central to the transcriptional response to hypoxia, while in ECs, both HIF1-α and HIF2-α are required to coordinate an acute and chronic response, respectively. In this setting, GATA2-AS1 contributes to the "HIF switch" and augments HIF1-α induction in acute hypoxia to regulate HIF1-α/HIF2-α balance. In hypoxia, GATA2-AS1 orchestrates HIF1-α-dependent induction of the glycolytic pathway and HIF1-α-independent maintenance of mitochondrial biogenesis. Similarly, GATA2-AS1 coordinates both metabolism and "tip/stalk" cell signaling to regulate angiogenesis in hypoxic ECs. Furthermore, we find that GATA2-AS1 expression patterns are perturbed in atherosclerotic disease. Together, these results define a role for GATA2-AS1 in the EC-specific response to hypoxia.
Long noncoding RNAs (lncRNAs) that have little to no protein-coding capacity have emerged as major features of eukaryotic genomes and important regulators of development, homeostasis, and disease (1)(2)(3)(4)(5). lncRNAs are loosely defined as RNA transcripts longer than 200 nucleotides that are not translated into protein (6). Current catalogues suggest that there are 17,944 to 27,919 lncRNAs in the human genome (7,8) that are often expressed in a tissue-specific manner (9)(10)(11)(12).
Several lncRNAs have now been described in vascular endothelial cells (ECs) and have roles in endothelial gene regulation and physiologic function (6). These include modulation of tip and stalk cell biology in angiogenesis (13)(14)(15). Other lncRNAs are responsive to changes in environmental conditions such as hypoxia and shear stress (13)(14)(15)(16).
We recently identified a set of EC-enriched lncRNAs and described the role of the STEEL lncRNA in angiogenic patterning (15). STEEL is expressed from the HOX loci, which specify regional identities in development (17), and ties EC angiogenic potential to shear stress conditions (15).
To further elucidate the role of EC-enriched lncRNAs in EC biology, we describe GATA2-AS1, an EC-enriched lncRNA expressed from the GATA2 genomic locus. GATA2 is one of six GATA transcription factors and is critical for hematopoiesis (18)(19)(20)(21) and differentiation of endothelial and related lineages (22). Of note, single nucleotide variants (SNVs) at this locus are associated with early-onset coronary artery disease (CAD) and lower expression of GATA2-AS1. We find that GATA2-AS1 modulates the EC hypoxic response by augmenting HIF1-α protein expression and thus affecting HIF1-α/ HIF2-α balance. In this way, GATA2-AS1 sets the stage for an EC-specific response to hypoxia. Importantly, we find disrupted expression patterns of GATA2-AS1 in vascular disease.

Genetic variation of GATA2-AS1
The GATA2 genomic locus has been identified as conferring susceptibility to human genetic disease (18,30). Three conserved GATA switch sites proposed to determine GATA2 expression lie within the GATA2-AS1 genomic locus (18). At one of these sites, −3.9 kb relative to the endothelial GATA2 TSS, we found several SNVs with a common (>5%) minor allele frequency ( Figs. 2A and S5). These sites fall within exon 3 of GATA2-AS1 but do not involve canonical W[GATA]R GATA-binding sequences. Individuals from Africa harbor the most variant sites, consistent with the out-of-Africa model of human origins (31).
To assess the effect of SNVs on GATA2-AS1 expression, we sequenced cDNA from HUVEC cell lines and found a 2:1 ratio of peak heights for the major:minor allele at positions +689, +775, and +846 in heterozygotes, indicating higher expression of the major allele at these positions (haplotypes 1 & 2 versus haplotype 3) (Fig. 2D). We found no difference in expression between haplotypes 1 and 2 (Fig. S5D). Notably, the haplotype that carried the minor allele at all four of these SNVs (haplotype "ACGG") occurred at a markedly lower frequency than the other haplotypes and had a different predicted secondary structure compared with major haplotypes (Figs. S6B and S7) (33). Thus, disease-associated minor alleles within GATA2-AS1 exon sequences are associated with GATA2-AS1 expression.

Evidence for antisense lncRNA expression in GATA loci
The six GATA genes in vertebrate genomes represent two classes, GATA123 and GATA456, that originated from two ancestral deuterostome genes via genome duplication events (34). GATA6-AS has been reported as a hypoxia-regulated lncRNA (13). Therefore, we asked whether antisense lncRNA expression was a feature of other GATA loci in humans and found that 4/6 GATA transcription factors, spanning both GATA classes (GATA123 and GATA456), have annotated antisense lncRNAs (Fig. S8). These include GATA2, GATA3, GATA5, and GATA6.
Because GATA genes evolved via duplication events, these findings in the human GATA loci could reflect antisense regulatory elements that were also duplicated in evolution. As a result, we examined GATA gene loci across vertebrate evolution using existing public databases and looked for lncRNAs within a 5-kb window of GATA transcription factors. A GATA2-AS lncRNA was conserved in some primates. We found a syntenic ortholog for GATA2-AS1 in mouse (90 million years), with 30% RNA sequence conservation in at least one isoform (Fig. S9A), consistent with RNA sequence variation of lncRNAs forming an important component of speciation. Overall, we found evidence of antisense lncRNA expression at GATA factor loci across 300 million years of evolution between humans and chicken (Fig. S9B). All GATA loci demonstrated expression of an antisense lncRNA in at least one examined species except GATA1. However, there does not appear to be any common lineage of GATA-antisense lncRNAs between closely related species, in keeping with rapid evolution of lncRNA loci (35). This interspecies variation in lncRNA loci is consistent with intraspecies variation described above. Figure 1. GATA2-AS1 is an endothelial-enriched, predominantly cytoplasmic long noncoding RNA expressed from the GATA2 locus. A, schematic representation of the GATA2-AS1 locus. GATA2-AS1 is transcribed antisense to GATA2. There are 1234 bp separating the transcription start site of GATA2-AS1 from the GATA2 IG promoter. There is no exon-exon overlap between GATA2-AS1 and GATA2 variants. GATA binding sites (W[GATA]R) are depicted as green bars. B and C, real-time quantitative PCR showing endothelial cell-enriched expression of GATA2-AS1 (B) and GATA2 (C). Kruskal-Wallis test, SEM, n =3. D, single molecule RNA-FISH of GATA2 (red) and GATA2-AS1 (green) in HUVECs. Top panels show GATA2 and GATA2-AS1 expression at 20× magnification. The top scale bar represents 50 μm. Colocalization is shown in the last column. Bottom panels show higher-resolution images focused on a single cell at 63× magnification. Both GATA2 and GATA2-AS1 RNA can be seen in the cytoplasm and at high abundance in a single cell. The bottom scale bar represents 10 μm. Images taken with Spinning Quorum Disc Confocal Microscope with a 20× objective with 0.6 numerical aperture and 63× oil immersion objective with 1.4 numerical aperture at room temperature and an EM-CCD camera (Hamamatsu ImageEMX2). Stains used were DAPI (405), Green/GATA2-AS1 (Alexa 488), Red/GATA2 (Cy3-555). Images were analyzed in Imaris Image Analysis Software and FIJI. HASMC, human aortic smooth muscle cell; HMVEC, human dermal microvascular endothelial cell; HUVEC, human umbilical vein endothelial cell.  (Table S4). The position of each SNV relative to the GATA2-AS1 transcription start site (TSS) is shown. B, frequency of common haplotypes of SNVs within GATA2-AS1 across the 1000 genomes dataset (n = 2504). Three major haplotypes are seen. Four SNVs within GATA2-AS1 exons are indicated with their positions versus the TSS (+689, +729, +775, +846). C, SNVs within the GATA2-AS1 upstream region (rs1573858) and intron 2 (rs6439129) that have been associated with disease susceptibility for early-onset coronary artery disease are shown. These disease-associated SNVs are in linkage disequilibrium with SNVs within GATA2-AS1 exons. Disease alleles are associated with Haplotype 3. D, sequencing of genomic DNA and RNA from human umbilical vein endothelial cells (HUVECs). Example of a heterozygote for haplotype 2/3 is shown. The genomic sequencing (top) shows equal peak heights for bases representing both alleles at each SNV site (+689, +729, +775). The RNA sequencing (bottom) shows bigger peak heights for bases representing alleles for Haplotype 2 (GCAT) versus Haplotype 3 (ATGG) at each SNV site. All three HUVEC lines with these heterozygous haplotypes demonstrated similar results with a 2:1 ratio of sequencing peak heights for Haplotype 1 or Haplotype 2: Haplotype 3. AFR, Africa; AMR, America; EAS, East Asian; EUR, European; SAS, South Asian. GATA2-AS1 regulates endothelial hypoxic signaling of HUVEC showed that GATA2-AS1 was expressed in the same clusters as GATA2 (Fig. S3C). We confirmed that GATA2-AS1 and GATA2 are coexpressed in the same cells using smRNA-FISH. Overall, 39% of cells express GATA2-AS1 (Fig. 1D) and 61% of cells express GATA2. Of cells expressing GATA2-AS1, a majority (78%) also express GATA2, and of cells that express GATA2, half (50%) express GATA2-AS1 (Figs. 1D and S3D). A similar degree of overlap was noted in scRNAseq of HUVEC, although the absolute fraction of cells expressing GATA2-AS1 or GATA2 was lower, possibly reflecting lower sensitivity of scRNAseq compared with smRNA-FISH (37). These results demonstrate that GATA2-AS1 and GATA2 expression are not mutually exclusive.
GATA2 and GATA2-AS1 have distinct target genes LncRNAs can act in cis to regulate neighboring genes (9,11,38). To determine if there was regulation of GATA2 expression by the GATA2-AS1 transcript, we depleted GATA2-AS1 using siRNA. We found a small decrease in GATA2 mRNA levels but note an increase in GATA2 protein levels (Figs. 3, A and B and S10, A and B). GATA2 is known to bind to genomic regions corresponding to exon 3 of GATA2-AS1 in ECs (18). To determine an effect of GATA2 on GATA2-AS1 expression, we used siRNA targeting GATA2 and found no major effects on GATA2-AS1 RNA expression (Fig. 3C). While these results do not rule out regulation of GATA2-AS1 by GATA2, GATA2 does not appear to be a critical activator of GATA2-AS1 in ECs.
A high degree of overlap between GATA2-AS1 and GATA2 targets genome-wide could also suggest cis-regulation or synergy as a major feature of this locus. We used siRNA knockdown of GATA2-AS1 in HUVEC followed by microarray analysis to assess GATA2-AS1 targets on a genome-wide scale. Overall, 1374 protein-coding mRNAs and 375 lncRNAs were regulated in common between two siRNA sequences targeting GATA2-AS1 (Figs. 3, D and E and S10, C and D). Of those, 782 protein-coding mRNAs and 166 lncRNAs were upregulated, and 592 and 209 were downregulated, respectively. Gene Ontology analysis revealed GATA2-AS1 target genes function across a variety of intracellular processes, including response to stress (Fig. S10D) (39).
We compared our list of target genes to published GATA2 targets in HUVEC (GEO Accession: GSE29531) (40), which identified 116 GATA2 target genes, and found 11 proteincoding genes in common (Figs. 3F, S11 and S12). While some target genes are shared, the majority of GATA2-AS1 and GATA2 regulated protein-coding genes appear distinct, although technical differences in microarray platform and hybridization may underestimate the overlap in target genes.

GATA2-AS1 modulates HIF1-α induction in acute hypoxia
Microarray in HUVEC showed that HIF1A mRNA was downregulated with GATA2-AS1 knockdown in normoxia (Fig. 3D). However, HIF biology is regulated at the level of the protein, whereby HIF-α proteins are constitutively expressed and stable in the absence of oxygen but degraded in the presence of oxygen (43,44). Accordingly, we sought to assess the regulation of HIF1-α protein by GATA2-AS1 under hypoxic conditions. We depleted GATA2-AS1 RNA in normoxia and acute (4 h) and chronic (24 h) hypoxia (<1% O 2 ). GATA2-AS1 knockdown decreased HIF1-α protein induction with acute hypoxia (Fig. 4A). HIF1-α levels were unaffected by GATA2-AS1 knockdown with chronic hypoxia. Two factors may contribute to this observation. First, HIF1-α protein expression is lower with 24 h versus 4 h hypoxia, consistent with the literature (45). Second, GATA2-AS1 RNA levels are already decreased with 24 h of hypoxia (Fig. 4A). Nonetheless, this effect appears to be specific to HIF1-α as HIF2-α protein induction did not decrease with GATA2-AS1 knockdown.
Consistent with previous data (46), we found a decrease in HIF1A but not EPAS1/HIF2A mRNA with acute and chronic hypoxia. Knockdown of GATA2-AS1 with hypoxia did not significantly change HIF1A or HIF2A mRNA levels and suggests posttranscriptional regulation of HIF1-α protein levels, consistent with the cytoplasmic localization of GATA2-AS1 RNA (Fig. S14, A-C). As reviewed by us and others, the cytoplasmic effects of lncRNAs are protean, so in future, the mechanism of GATA2-AS1 effects in acute hypoxia will be of great interest (47). Overexpression of GATA2-AS1 did not increase HIF1-α protein levels in acute hypoxia, suggesting a threshold effect. However, there was a small but variable increase in HIF1-α protein levels by 1.6-fold relative to control with chronic hypoxia when GATA2-AS1 and HIF1-α protein levels are normally reduced (Fig. S15, A and E). Thus, GATA2-AS1 overexpression maintained HIF1-α protein levels in chronic hypoxia at a similar level to acute hypoxia. In summary, we find a major effect of GATA2-AS1 in priming acute HIF1-α protein induction in response to acute hypoxia.

GATA2-AS1 maintains the expression of glycolytic pathway genes in hypoxia
In hypoxia, ECs demonstrate increased anaerobic glucose metabolism through increased expression of HIF1-α target genes (48) such as HK1 and LDH (49). Indeed, GATA2-AS1 knockdown with hypoxia decreased many HIF1-α-specific target genes in the glycolytic pathway, such as HK1, HK2, LDHA, PGK1, and MCT4 (Figs. 4, B and C and S14, E-H). The HIF2-α response is an organismal response, and characterized by EPO, which increased with GATA2-AS1 knockdown. A greater decrease in mRNA levels of target genes at 24 h hypoxia may result from persistent mRNA at 4 h hypoxia due to GATA2-AS1 regulates endothelial hypoxic signaling GAPDH is shown as a loading control. Right, quantitation of Western blots showing GATA2 protein levels. C, effect of GATA2 knockdown by siRNA on GATA2-AS1 RNA levels. GATA2 knockdown by siRNA did not significantly alter GATA2-AS1 RNA expression. One-way ANOVA, SEM, n = 3. Normality tested with Shapiro-Wilks test. D, volcano plot showing microarray analysis of differentially regulated mRNAs with GATA2-AS1 knockdown. Shown are mRNAs differentially regulated by both GATA2-AS1 siRNAs. A total of 782 protein-coding mRNAs were upregulated, and 592 were downregulated 2-fold or more. Statistical significance was assessed by Student t test with a Benjamini-Hochberg adjusted false discovery rate <0.05. Gray dots indicate hits that fell below fold change and significance cutoffs. Blue dots indicate hits that exceed fold change and significance cutoffs in at least one siRNA, and red dots indicate hits that exceed fold change and significance cutoffs with both siRNAs that target GATA2-AS1. E, heatmap showing microarray data from GATA2-AS1 knockdown. Select upregulated and downregulated genes are shown. Expression level represents z-score of expression for each gene. F, Venn diagram showing overlap of GATA2-AS1 mRNA target genes and GATA2 target genes (26). In human umbilical vein endothelial cells, GATA2-AS1 regulated 11/116 (9.5%) of GATA2 target genes. Unless otherwise stated, statistical significance was assessed by two-sided Student t test and **p < 0.01. mRNA half-life despite a decrease in HIF1-α-mediated transcription. These results suggest a role for GATA2-AS1 in regulating HIF1-α-mediated metabolic reprogramming in response to hypoxia and the balance of HIF1-α and HIF2-α signaling.

GATA2-AS1 maintains mitochondrial mass in hypoxia
A decrease in oxidative phosphorylation and increased mitophagy is a HIF1-α-dependent event in hypoxia, in part through increased expression of BNIP3 ( Fig. 5A) (50). In ECs, although there is a decrease in mitochondrial respiration with hypoxia, there is no change in mitochondrial biogenesis (49). Although ECs are highly glycolytic even in the presence of oxygen, mitochondria act as important signaling organelles in ECs (51,52), including the stabilization of HIF (43,53). As a result, we assessed the role of GATA2-AS1 on mitochondrial dynamics. Although we found variable effects of GATA2-AS1 knockdown on BNIP3 and BNIP3L mRNA in hypoxia (Fig. 5, B and C), we found a decrease of both BNIP3 and BNIP3L protein expression in acute and chronic hypoxia. As a result, we expected that GATA2-AS1 knockdown would lead to an increase in mitochondrial mass and function in hypoxic conditions. Surprisingly, GATA2-AS1 knockdown led to a 50% decrease in mitochondrial mass and a trend toward decreased mitochondrial membrane potential (Fig. 5, D and E) with 24 h hypoxia. Thus, GATA2-AS1 contributes to the maintenance of mitochondrial mass in hypoxia, likely through HIF1-α-independent mechanisms.

GATA2-AS1 modulates angiogenic patterning and signaling in hypoxia
Sprouting angiogenesis expands the vascular network in hypoxic tissues. HIF1-α and HIF2-α differentially regulate vessel growth with HIF1-α promoting EC migration and HIF2α promoting vessel integrity (48,(54)(55)(56). In keeping with these effects on HIF1-α, GATA2-AS1 knockdown led to decreased Transwell migration of HUVEC in hypoxia (Figs. 6, A-C and S16, A-C). In the tip/stalk paradigm of angiogenesis (57), tip cells rely more on anaerobic glycolysis while stalk cells use fatty acid oxidation (51). Therefore, we expected GATA2-AS1 knockdown to decrease tip cell balance in hypoxia due to its effect on HIF1-α and glycolytic genes. Unexpectedly, we found that GATA2-AS1 knockdown in chronic hypoxia increased the number of sprouts/spheroid in a sprouting assay (Fig. 7, A and  B). Thus, the effect of GATA2-AS1 on spheroid sprouting in hypoxia does not appear to be purely dependent on its effect on HIF1-α signaling. To further investigate the role of GATA2-AS1 on sprouting, we assessed tip and stalk cell gene expression by RT-qPCR. GATA2-AS1 knockdown in hypoxia led to an increase in tip cell genes such as DLL4 and KDR/VEGFR2, as well as stalk cell genes such as NOTCH1 and sFLT1/ sVEGFR1 (51,58), consistent with the results of the sprouting assay (Fig. 7C). These findings suggest both HIF1-α-dependent and HIF1-α-independent effects of GATA2-AS1 in hypoxic signaling and demonstrate a higher-order role for GATA2-AS1 in orchestrating the EC-specific response to hypoxia.
Because GATA2-AS1 is heterogeneously expressed in ECs (Figs. 6D and S3, C-E), we looked for coexpression of GATA2-AS1 with tip and stalk cell genes in normoxia with scRNAseq (Fig. 7D). GATA2-AS1 expression is enriched in clusters 1 to 3, whereas tip and stalk cell genes are most enriched in clusters 4 to 6, which appear to have the greatest angiogenic potential and are more closely related clusters (Fig. S16, A-C). Nonetheless, there is overlapping expression of GATA2-AS1 with tip and stalk cell genes across clusters. Whether GATA2-AS1 acts in a paracrine fashion is yet to be determined, although we note expression of GATA2-AS1 in blood exosomes (Fig. 8A).

GATA2-AS1 is differentially expressed in human disease samples
Given that SNVs in GATA2-AS1 reduce GATA2-AS1 expression and are associated with early-onset CAD, we assessed GATA2-AS1 expression in human disease samples. First, we assessed whether GATA2-AS1 could function as a biomarker for CAD. We queried exoRBase (59) and found that exosomal expression of GATA2-AS1 was higher in coronary heart disease compared with healthy populations (Healthy, WB) ( Fig. 8A and S17A). Superficially, these results do not fit the paradigm that alleles with lower expression of GATA2-AS1 in HUVEC are associated with early-onset CAD. However, it is not known how GATA2-AS1 contributes to early-onset CAD. The molecular pathology of disease pathogenesis in earlyonset CAD may be different from that found in established disease. Furthermore, the cellular source of GATA2-AS1 is unknown in these disease samples. Finally, the risk of CAD in humans is higher (60) than the minor allele frequency of GATA2-AS1 variants. Thus, while SNVs within GATA2-AS1 are associated with early-onset CAD, the genetic background of samples from exoRBase may represent alternative pathogenesis of disease.
Next, we evaluated GATA2-AS1 expression in ECs from atherosclerosis tissue from scRNAseq samples of diseased human carotid arteries (GSE159677). Compared with atherosclerotic core, there was greater expression of GATA2-AS1 in two EC clusters from control tissue (E.3, E.4) and decreased expression in one EC cluster (E.5) (Fig. 8B). A similar pattern was seen with GATA2 in clusters E.3 and E.4 but not E.5. As with our data, there was some overlap of tip/stalk cell genes in clusters that express GATA2-AS1, but the pattern of expression between control and atherosclerosis core was different with GATA2-AS1 knockdown in acute and chronic hypoxia. D, left, representative Western blots of HK2 and LDHA with GATA2-AS1 siRNA knockdown in normoxia, acute (4 h) and chronic (24 h) hypoxia. α-Tubulin is shown as a loading control; loading control for HK2 corresponds to the loading control for BNIP3L (Fig. 5) and loading control for LDHA corresponds to the loading control for BNIP3 (Fig. 5). Consistent with mRNA levels, there is a decrease in HK2 and LDHA protein with siGATA2-AS1, especially in hypoxia. Middle, QUANTITATION of Western blots for HK2 show that GATA2-AS1 siRNA knockdown decreases HK2 induction in chronic (24 h) hypoxia. Right, quantitation of LDHA protein shows trend toward a decrease in LDHA levels with GATA2-AS1 knockdown in acute and chronic hypoxia. E, RT-qPCR of HK2 and LDHA with GATA2-AS1 knockdown in acute and chronic hypoxia. Statistical significance between siCtl and siGATA2-AS1 was assessed by two-way ANOVA, SEM, n = 3. Normality tested by Shapiro-Wilks test. RT-qPCR, real-time quantitative PCR.
GATA2-AS1 regulates endothelial hypoxic signaling Figure 5. GATA2-AS1 maintains mitochondrial mass in hypoxia. A, schematic showing expected relationship of hypoxia with mitochondrial dynamics through HIF signaling. B, left, representative Western blot of BNIP3L and BNIP3 with GATA2-AS1 knockdown in normoxia and hypoxia. BNIP3L and BNIP3 are HIF1-α-specific target genes. α-Tubulin is shown as a loading control; loading control for BNIP3 corresponds to loading control for LDHA (Fig. 4) and loading control for BNIP3L corresponds to loading control for HK2 (Fig. 4). There is a decrease in BNIP3L and BNIP3 protein with GATA2-AS1 knockdown in hypoxia. Middle, quantitation of Western blots for HIF1-α show that GATA2-AS1 siRNA knockdown decreases BNIP3L induction in hypoxia. Right, quantitation of BNIP3 protein shows that GATA2-AS1 knockdown decreases BNIP3 induction in hypoxia. C, real-time quantitative PCR of BNIP3L (left) and BNIP3 (right) with GATA2-AS1 knockdown in acute and chronic hypoxia. D, fluorescence measurements of Mitotracker Green FM in human umbilical vein endothelial cells with GATA2-AS1 knockdown in normoxia and 24 h hypoxia. Mitotracker tracks mitochondrial abundance independent of membrane potential. Hoechst staining was used to normalize for cell number. There was a decrease in Mitotracker Green FM staining with GATA2-AS1 knockdown, especially in hypoxia. E, fluorescence measurements of JC-1 staining with GATA2-AS1 knockdown in normoxia and 24 h hypoxia. JC-1 staining reflects mitochondrial membrane potential and is represented as a red/green ratio. JC-1 fluoresces green in isolation but aggregates and fluoresces red in the presence of negative charge. A higher red/green ratio indicates presence of mitochondrial membrane potential. There was a trend toward a decrease in mitochondrial membrane potential with GATA2-AS1 knockdown, especially with hypoxia. Statistical significance between siCtl and siGATA2-AS1 was assessed by two-way ANOVA or two-sided t test, SEM, n = 3. Normality tested by Shapiro-Wilks test. (Fig. S17B). Similarly, there is differential expression of GATA2-AS1 and GATA2 in endothelial cells from human aortic aneurysms (Fig. S17C, GSE155468). Thus, the pattern of GATA2-AS1 expression within clusters of ECs is perturbed in ECs from atherosclerotic tissue.

Discussion
A growing body of evidence implicates lncRNAs as important mediators of endothelial biology (6, 13-16, 61, 62). Here, we describe GATA2-AS1, an EC-enriched lncRNA that orchestrates HIF1-α-dependent and HIF1-α-independent Figure 6. GATA2-AS1 increases endothelial cell migration in hypoxia. A, schematic representation of Transwell migration assay. Images were acquired on a Nikon Eclipse TS100 with a 20× objective and a Nikon Digital Sight camera. B, images and quantitation of Transwell migration of human umbilical vein endothelial cells (HUVEC) with GATA2-AS1 knockdown in normoxia. GATA2-AS1 knockdown led to an increase in Transwell migration in normoxia. Statistical significance was assessed by two-sided t test, SEM, n = 2 biological replicates, n = 9 technical replicates. Normality tested by Shapiro-Wilks test. C, images and quantitation of Transwell migration of HUVEC with GATA2-AS1 knockdown in hypoxia. GATA2-AS1 knockdown led to a decrease in Transwell migration in hypoxia. A minimum of nine wells were assessed for each condition with six images taken per well. D, single-cell RNA-sequencing data of GATA2-AS1 and GATA2 expression. In freshly isolated HUVEC (Passage 0 = P0), GATA2 was expressed in 69% of cells and GATA2-AS1 was expressed in 41% of cells. In Passage 3 (P3) HUVEC, GATA2 was expressed in 30% of cells, GATA2-AS1 was expressed in 21% of cells. For technical reasons, functional studies of GATA2-AS1 were performed in passaged HUVEC. Unless otherwise stated, statistical significance was assessed by two-sided Student t test and * p < 0.05. Figure 7. GATA2-AS1 increases endothelial sprouting in hypoxia. A, representative images from spheroid sprouting assay in human umbilical vein endothelial cells (HUVEC) with GATA2-AS1 knockdown in normoxia and hypoxia. The scale bar represents 100 μm. B, quantitation of spheroid sprouting assay (n = 50-100 spheroids per condition) with GATA2-AS1 knockdown in normoxia and hypoxia. There was an increase in the number of sprouts with GATA-2AS1 knockdown in hypoxia. Kruskal-Wallis test, n = 3. Videos of sprouting assay in normoxia can be seen in Video S1 and S2. Images were acquired on a Zeiss AxioObserver Live Cell with FLIM with a 20× objective at 37 C and analyzed with FIJI. C, real-time quantitative PCR of "tip" and "stalk" cell markers during angiogenesis. GATA2-AS1 knockdown led to increased expression of both "tip" and "stalk" cell genes in acute (4 h) hypoxia. Statistical significance between siCtl and siGATA2-AS1 was assessed by two-way ANOVA, SEM, n = 3. D, single-cell RNA sequencing of cultured HUVEC. Cultured HUVEC display heterogenous gene expression patterns with nine different clusters. Clusters 4 to 6 are associated with increased expression of tip cell genes. Stalk cell genes are expressed across clusters 1 to 7. GATA2-AS1 is most highly enriched in clusters 1 and 3 but is also expressed in clusters 1 to 7.
signaling with hypoxia. One of the more abundant EC-enriched lncRNAs, GATA2-AS1, sets the stage for coordinated sprouting angiogenesis in hypoxia by regulating the endothelial metabolic apparatus and gene expression networks.

Contribution of GATA2-AS1 to the HIF switch
In most cells, HIF1-α is central to the transcriptional response to hypoxia, while in ECs, both HIF1-α and HIF2-α are required. A rapid reduction in HIF1-α leads to the switch Figure 8. GATA2-AS1 is differentially expressed in human disease samples. A, boxplots of data from exoRBase (http://www.exorbase.org accessed June 23, 2021) show increased expression of GATA2-AS1 in exosomes of patients with coronary heart disease (CHD) compared with two control groups, healthy controls (Healthy) and whole blood (WB). For comparison GATA2 mRNA expression is shown. There was no significant difference in GATA2 mRNA expression in exosomes across these samples. Other endothelial-enriched genes (NOS3/eNOS, STEEL, DLL4) and the ANRIL lncRNA are shown for comparison. B, dot plot and violin plots from single-cell RNA sequencing of human atherosclerosis tissue show differential expression of GATA2-AS1 (left) and GATA2 (right) in endothelial cell clusters (E.1-E.6). In clusters E.3 and E.4 there is greater expression of GATA2-AS1 in control tissue compared with atherosclerosis core, and in E.5, there is greater expression of GATA2-AS1 in atherosclerosis core. Data were accessed from GEO (GSE159677) control, proximal adjacent tissue from the carotid artery. Athero, atherosclerosis core; CHD, coronary heart disease; CRC, colorectal cancer; HCC, hepatocellular carcinoma; PAAD, pancreatic adenocarcinoma; WB, whole blood Unless otherwise stated, statistical significance was assessed by two-sided Student t test.
between HIF1-α and HIF2-α signaling from acute to chronic hypoxia (43,46). HIF1-α and HIF2-α have distinct, cell-specific responses. HIF1-α functions to maintain cell survival through its roles in metabolism and vessel sprouting, whereas HIF2-α functions to maintain tissue survival through its roles in the vascular response to hypoxia (63). Indeed, cell death ensues when HIF1-α levels are not reduced in chronic hypoxia (64). We find that GATA2-AS1 contributes to HIF1-α, but not HIF2α, induction with acute hypoxia. Subsequently, both GATA2-AS1 and HIF1-α levels are reduced in chronic hypoxia. In this way, GATA2-AS1 contributes to the balance of HIF1-α to HIF2-α in the "HIF switch" mechanism (65,66). Accordingly, we find that GATA2-AS1 is a key factor in the upregulation of the anerobic glycolytic pathway with hypoxia (67).
However, GATA2-AS1 has HIF1-α-independent regulation. Even in normoxia, ECs are highly glycolytic (51) and have low mitochondrial mass, consistent with a role for endothelial mitochondria in signaling rather than energy production (52,68). In some cell types, HIF1-α is a key factor in reducing mitochondrial mass in hypoxia through mitophagy (50). While the effects of GATA2-AS1 on BNIP3/BNIP3L were in keeping with HIF1-α induction, GATA2-AS1 maintains mitochondrial mass and membrane potential with hypoxia. In addition to increases in VEGF (69), these findings provide another pathway by which mitochondrial biogenesis in hypoxia is maintained in ECs (49). Thus, GATA2-AS1 positions ECs for an appropriate metabolic response to hypoxia. HIF1-α is ubiquitously expressed across cell types (44), yet these mitochondrial dynamics are a cell-specific response. Together, these results support GATA2-AS1 as a mediator of the ECspecific response to hypoxia.

GATA2-AS1 mediates a balance of angiogenic processes in hypoxia
In sprouting angiogenesis, sprouts are led by a tip cell and elongated by proliferating stalk cells (70). Both glycolysis and reactive oxygen species are metabolic drivers of the tip cell phenotype (51,68,70). Tip cell formation is a dynamic and competitive process (71), whereby tip and stalk selection dynamically respond to external signals on a heterogeneous background of tip and stalk susceptibility. A failure to regulate this process can lead to pathologic vascular hypersprouting (70).
GATA2-AS1 augments HIF1-α induction and upregulates the glycolytic pathway. As glycolysis drives sprout numbers, tip cells experience a high glycolytic flux during angiogenesis. In addition, compartmentalized glycolysis within filopodia and lamellipodia facilitates migration (51). Accordingly, we found that GATA2-AS1 increased EC migration in hypoxia. Thus, GATA2-AS1 may facilitate the cooperation of heterogenous ECs during angiogenesis, each following a different metabolic program (72).
These observations suggest that GATA2-AS1 would increase tip susceptibility, sprout numbers, and pathologic hypersprouting. However, we found that GATA2-AS1 inhibited sprout number in hypoxia. Indeed, GATA2-AS1 inhibited the expression of both tip and stalk cell genes in keeping with scRNAseq data that GATA2-AS1 is expressed across all EC clusters, including tip and stalk clusters. One explanation is that GATA2-AS1-enriched ECs are positioned to limit sprout number in an HIF1-α-independent manner. Overall, this work suggests that heterogeneous expression of GATA2-AS1 provides one path to control the metabolic and molecular coordination of sprouting.

Disease associations of GATA2-AS1
Genome-wide association studies and updated lncRNA catalogues suggest that 40.7% of lncRNAs are associated with a trait (8). Genetic variation of the lncRNA ANRIL (CDKN2B-AS1) first highlighted the potential role of lncRNAs in cardiovascular disease (73). Over a decade since this discovery, we are still learning the pathogenic contribution of ANRIL to this multifactorial disease (74). Here, we find a disease association for GATA2-AS1 both with the development of cardiovascular disease and in established cardiovascular disease.
First, we find that SNVs in GATA2-AS1 exons are associated with early-onset CAD (32) and decreased GATA2-AS1 expression. Next, we find that GATA2-AS1 expression is perturbed in tissue from patients with established cardiovascular disease. Depending on the cellular source, exosomes can contribute to both prevention of atherosclerosis or activation of thrombogenesis (75). Whether GATA2-AS1 in this context plays a protective or pathogenic role remains to be determined. The data presented here represent initial studies to describe a role for the endothelial-enriched lncRNA GATA2-AS1 in endothelial gene regulation and disease relevance. Future work with diseased tissue, disease models, and endothelial cell types will help to further elucidate the role of GATA2-AS1 in cardiovascular disease. Overall, this evidence links GATA2-AS1 with cardiovascular disease and provides rationale for future studies.
Overall, these findings provide further evidence for a coordinating role of lncRNAs in mediating cell type-specific responses. Future studies will be important to determine the mechanisms by which GATA2-AS1 may contribute to disease development and/or recovery.

Cell culture
HUVECs at early passage (passages 2-5) were isolated and cultured from multiple independent donors as per (76) (St Michael's Hospital, REB# 03-201). Primary cultures of endothelial cells were plated on 100-mm tissue culture dishes precoated with 0.2% gelatin. Cells were maintained in Endothelial Cell Growth Medium 2 (PromoCell, Cat# C-22011). All cell cultures were maintained at 37 C in a humidified 5% CO 2 chamber. For hypoxia treatment, cells were subjected to <1% O 2 in a temperature-and humidity-controlled incubator within a sealed anaerobic system (Thermo Forma model 1025) using a high-purity anaerobic gas mixture (5% CO 2 , 10% H 2 , 85% N 2 ; Linde).
ECs were cultured in base media supplemented with arterial (100 ng/ml VEGFA, 30 ng/ml bFGF) or venous (10 ng/ml VEGFA, 30 ng/ml bFGF) growth and maintenance factors. Cells were grown for 6 days (day 14 of differentiation) and at this point were dissociated and examined for endothelial purity (>80% CD34+CD31/PECAM1+) and maintenance of arteriovenous identity (arterial ECs were CD184/CXCR4 high CD73/ NT5E mid, and venous ECs were CD184/CXCR4 low/negative CD73/NT5E high). Arterial-like and venous-like ECs from exclusively hypoxic culture up to this point were replated on fibronectin-coated glass slides. Cells were grown to confluence at 20% O2 for 24 h and were then moved to respective flow ± hypoxia in vitro setups for further study.

RNA isolation
Total cellular RNA was harvested using the RNeasy Mini Kit (Qiagen, Cat# 74104) according to the manufacturer's instructions. Reverse transcription was done with 100 ng to 1 μg of total RNA using the SuperScript III First-Strand Synthesis SuperMix for RT-qPCR (Thermo Fisher Scientific, Cat# 11752-050) according to the kit instructions. RT-qPCR was performed using the QuantStudio 7 Flex Real-Time PCR System (Applied Biosystems).
DNA isolation, PCR, and sanger sequencing to detect single nucleotide variants Cells were lysed using standard phenol/chloroform and salt-ethanol precipitation methods.
The primer pairs that were used to PCR amplify heterozygous SNVs from HUVEC cells using Platinum Taq DNA Polymerase High Fidelity (Thermo Fisher Scientific, Cat# 11304011) can be found in Table S1. PCR products were Sanger sequenced at The Centre of Applied Genomics (Peter Gilgan Centre for Research and Learning) using the corresponding primers in Table S1.
Single molecule RNA fluorescence In Situ hybridization FISH was performed using QuantiGene ViewRNA ISH Cell Assay Kit (Affymetrix, Cat# QVC0001) following the manufacturer's instructions. HUVECs were grown to confluence on gelatin-coated coverslips, then fixed in 4% paraformaldehyde and permeabilized. Probes were applied (probes, GATA2: VA1-1453, GATA2-AS1: VA4-6001024) and nuclei were stained with DAPI. Images were collected on the Spinning Quorum Disc Confocal Microscope using an EM-CCD camera (Hamamatsu ImageEMX2) with either a 20× objective (0.6 NA) or a 63× oil immersion objective (1.4 NA). Image analysis was conducted using Imaris Image Analysis software and FIJI.

Immunoblotting
Total cellular protein was isolated using RIPA buffer (Cell Signaling Technology). Proteins were size fractionated on NuPAGE Novex 4 to 12% Bis-Tris (Invitrogen) using Xcell SureLock Mini-Cell (Invitrogen) and transferred onto 0.45-μm nitrocellulose membranes using the Xcell II Blot Module (Invitrogen) according to manufacturer's recommendations. Membranes were incubated overnight with the one antibodies for proteins of interest (Table S2). Horseradish peroxidaseconjugated secondary antibodies for rabbit anti-mouse IgG (ab6728, Abcam), goat anti-rabbit IgG (sc-2004, Santa Cruz Biotechnology), or donkey anti-goat IgG (sc-2020, Santa Cruz Biotechnology) were incubated for 1 h at room temperature. Signal quantification was performed using either ImageJ (NIH) or Image Lab (Bio-Rad).

siRNA knockdown
HUVECs were grown to 90% confluency on 60-mm (2) gelatin-coated tissue culture plates and transfected with siRNA at a final concentration of 40 nM using Oligofectamine (Invitrogen) in a total volume of 2000 μl. Transfection occurred for 4 h at 37 C in Opti-MEM medium (Invitrogen), after which M199 medium (Invitrogen) containing fetal bovine serum (Hyclone), heparin, and endothelial cell growth supplement (Biomedical Technologies) was added. Cells were incubated with siRNA for 24 to 48 h depending on the downstream application. Custom Stealth siRNAs (Invitrogen) were used to knockdown GATA2-AS1. SiGENOME Non-Targeting siRNA #3 (Dharmacon) was used as control siRNA transfection (Table S3).

Single-cell RNA sequencing preparation and analysis
An independent line of passage 3 HUVECs were prepared for sequencing. Samples were prepared and submitted on the same day to Princess Margaret Genomics Centre. The Chromium Single Cell 3 0 Reagent Kits v2 User Guide (CG00052) was used, and sequencing libraries targeting 6000 cells per sample were used. This is a droplet-cell capture approach and uses a 3 0 end counting sequencing approach. Libraries were sequenced on Illumina HiSeq2500 instrument targeting 100,000 reads per cell. Raw Illumina sequencing data from Chromium Single Cell libraries were preprocessed using the CELLRANGER (v2.1.0) pipeline from 10× Genomics. FASTQ sequences were mapped to the GRCh38 human reference genome using STAR aligner (STAR v2.5.2b) (77). The quality was assessed using RNA-SeQC (v1.1.7) and SAMTOOLS (v1.3.1). Gene-barcode matrices were normalized according to the method by Lun et al. (78) using the R package SCRAN (v1.2.2). Low-quality cells with log-library sizes >4 median absolute deviations below the median loglibrary size, log-number of genes detected >4 median absolute deviations below the median, or high mitochondrial gene expression (UMI counts) were removed from the data sets. A total of 8281 cells passed filtering. Approximately 3627 genes were measured per cell with 37,151 reads per cell. Principal component analysis, clustering, differential gene expression, and universal manifold approximation and projection visualizations were performed using R packages, SCATER (v1.  (83). Loupe Cell Browser (v2.0.0) was used to view and explore the dataset to find significant genes, cell types, and subpopulations. Data for carotid atherosclerosis samples were accessed on GEO (GSE159677). Data for aortic aneurysm samples were accessed on GEO (GSE155468).

Poly-adenylated RNA fractionation
PolyA RNA was isolated using the PolyA Spin mRNA Isolation Kit (New England Biolabs). RNA samples were purified as described above for five biological replicates of HUVEC. RNA was fractionated according to manufacturer's instructions. cDNA was made from equal proportions of RNA based on the total mass collected from each fraction.

Nuclear/cytoplasmic partitioning
HUVECs were detached with 0.05% Trypsin and washed with PBS. A volume of 175 μl RLN buffer (50 mM Tris-HCl, pH 8.0; 140 mM NaCl; 1.5 mM MgCl2; 0.5% Nonidet P-40; 0.2 units/μl RNaseOUT; 1 mM dithiothreitol [DTT]) was used to resuspend pellet followed by a 5-min incubation on ice. Cellular debris and nuclei were pelleted at 300g for 2 min at 4 C. The cytoplasmic fraction was transferred to 600 μl Solution D (4 M guanidinium thiocyanate, 25 mM sodium citrate, pH 7; 0.5% sarcosyl, 0.1 M 2-mercaptoethanol) (84). The nuclear pellet was washed in 500 μl PBS, pelleted again at 300g for 3 min at 4 C, and resuspended in 600 μl Solution D. Both nuclear and cytoplasmic fractions were then subjected to RNA isolation as described above. Quantification of nuclear and cytoplasmic fractions was normalized relative to an equal percentage of RNA extracted from each fraction.

Transwell migration assay
Twenty-four Transwell plates (8 um pore size; BD Biosciences) were coated with 0.2% gelatin, and 10% fetal bovine serum was used as a chemoattractant in the lower chamber to initiate cell migration. A total of 50,000 HUVECs were plated and exposed to either normoxia or hypoxia for 16 h. The top of the inserts were cleared of cells with a cotton swab, fixed with 10% v/v of Formalin in PBS for 5 min. Migrated cells on the bottom of the insert were stained with 0.2% Crystal violet for 5 min. The migrated cells were quantified by counting six representative fields of each insert using scanned images (20× magnification). Results are expressed as an average of cell count per field, performed using two biological replicates with n = 9 inserts.

Nanostring
A Nanostring gene expression array was used to study the mRNA expression of 272 genes important in endothelial biology. A custom nCounter XT CodeSet was designed by the Bioinformatics team at Nanostring Technologies targeting 272 genes important in endothelial biology. RNA integrity number of the samples was assessed using the Agilent 2100 Bioanalyzer System. All samples had an RNA integrity number of 7.5 or higher, ensuring that intact RNA was used for the assay. The nCounter XT assay was performed as per the manufacturer's instructions. Data analysis was done using the nSolver Analysis Software provided by Nanostring. Quality control examined imaging efficiency, binding density, hybridization efficiency, and background fluorescence. Only samples that passed all quality control checkpoints were included in the analysis. Transcript counts were corrected for background fluorescence and normalized to synthetic positive controls to reduce protocol-related variations. Transcript counts were normalized to four housekeeping genes (B2M, HPRT, SDHA, and TBP that had a coefficient of variability <30%.

Mitochondrial assays
Mitotracker Green FM, 20 nM (Thermo Fisher, M7514) was used to stain HUVECs according to manufacturer's recommendations. JC-1, 5 μg/ml (Cayman Chemical, 15003) was used to stain HUVECs as in (85). Briefly, HUVECs were treated with siRNA for 24 h A total of 20,000 HUVECs were split into black-walled, clear bottom 96-well plates and left to adhere for 1 h. Cells were then treated with normoxia or hypoxia for a total of 24 h. Cells were washed with PBS+/+ and stained with either Mitotracker Green FM or JC-1 for 30 min and washed again with PBS+/+. Fluorescence was measured with a platereader (BioTek Multi-Mode HTX).

Spheroid assay
HUVECs were transfected with siRNA for 24 h and then trypsinized, resuspended in HUVEC medium, and counted with the Vi-Cell XR Cell Analyzer (Beckman Coulter). Using approximately 750 cells per spheroid, the cells were combined with M199 and methylcellulose [(Cells + M199 = 80%) + (methylcellulose = 20%)]. Spheroids were generated via the "hanging drop" method in an incubator at 37 C overnight. Spheroids were then pooled together and centrifuged at 500g for 3 min in PBS + 10% fetal bovine serum. The spheroids were then combined with methylcellulose, type I collagen (pH = 7), and VEGFA (100 ng/ml). The mixture was next plated onto an eight-well u-slide (Ibidi) and incubated at 37 C for 6 h, then they either remained in normoxia (21% O 2 ) or were transferred to hypoxia (1% O2) for 18 h. Spheroids were imaged using the Zeiss AxioObserver Live Cell with FLIM or the Nikon Eclipse TS100 with a 20× objective. Images were processed using FIJI.
ExoRBase GATA2-AS1 and GATA2 were searched on exoRBase (http://www.exorbase.org accessed June 23, 2021) (59). Visualizations of individual-level expression of GATA2-AS1 and GATA2 were exported as PDF. Individual level data were also exported, and differential expression between groups was plotted in GraphPad 8. Statistical difference between groups was calculated with one-way ANOVA in GraphPad 8.

Statistics
Unless otherwise stated, all experiments were performed in a minimum of three biological replicates. Unless otherwise indicated, the mean was calculated, and error bars indicate standard error (SEM). A p-value <0.05 was considered statistically significant.

Data availability
Microarray data are available under GEO Accession: GSE197376.